Typically, information about man-made, physical artifacts and/or objects is stored as data external to the object itself. Exceptions may include the object's chemical composition, or an identifying mark such as a signature. In some cases, its intended purpose may also be inferred from its shape. However, modern businesses often need to know much more about an object without referring to external data sets. This is particularly useful in three-dimensional (3D) printing, where a manufacturing device and/or a user may want to access the object's manufacturing information from the object itself, so as to enable quick and automated 3D printing.
To resolve this, businesses set up entire infrastructures to hold metadata (information about the classes of objects and individual objects themselves), or any other data needed or desired by the object's designer. This infrastructure is typically connected to an object via data matrix codes such as QR codes, bar codes, or radio frequency identification (RFID) tags that are positioned inside or on the object. However, these types of codes contain only a limited amount of information, and the user still needs to access external data sets to retrieve the complete information about an object. For example, even the highest density QR codes may only hold several thousand alphanumeric characters, and may only point to an external database address or link. Furthermore, such codes may be obtrusive in nature and are clearly visible.
Another drawback is that the infrastructures required to hold huge amounts of metadata are expensive, and so businesses may only save the metadata for a limited time. Once the time limit for storing the metadata for an object expires, it may become extremely difficult to get any information about an object.
The current disclosure discloses systems and methods to embed large volumes of data in a 3D object such that the object itself may function as a database.